from typing import Dict, List, Optional, Tuple
from enum import Enum
import asyncio
import json
import time


class TrainingType(Enum):
    LANGUAGE = "language"
    PROGRAMMING = "programming"
    INTERVIEW = "interview"
    SOFT_SKILLS = "soft_skills"
    EXAM_PREP = "exam_prep"


class DifficultyLevel(Enum):
    BEGINNER = "beginner"
    INTERMEDIATE = "intermediate"
    ADVANCED = "advanced"


class AITrainingCoach:
    def __init__(self, config: Dict):
        self.config = config
        self.user_level = DifficultyLevel.BEGINNER
        self.learning_progress = {}
        self.conversation_history = []

    async def initialize_session(self, user_id: str, training_type: TrainingType):
        """初始化训练会话"""
        self.current_user = user_id
        self.training_type = training_type
        self.session_start_time = time.time()

        # 加载用户历史进度
        self.learning_progress = await self._load_user_progress(user_id, training_type)

        return {
            "status": "session_started",
            "welcome_message": self._get_welcome_message(training_type),
            "current_level": self.learning_progress.get("level", "beginner")
        }


class ConversationEngine:
    def __init__(self):
        self.scenario_templates = self._load_scenario_templates()
        self.feedback_rules = self._load_feedback_rules()

    async def generate_training_scenario(self, training_type: TrainingType, level: DifficultyLevel) -> Dict:
        """生成训练场景"""
        template = self.scenario_templates[training_type][level]
        scenario = await self._fill_template(template)
        return scenario

    async def evaluate_response(self, user_input: str, expected_patterns: List[str]) -> Dict:
        """评估用户回复"""
        analysis = {
            "accuracy_score": 0,
            "fluency_score": 0,
            "improvement_suggestions": [],
            "correct_example": "",
            "next_question": ""
        }

        # 内容准确性评估
        accuracy = self._calculate_accuracy(user_input, expected_patterns)
        analysis["accuracy_score"] = accuracy

        # 流利度评估
        fluency = self._analyze_fluency(user_input)
        analysis["fluency_score"] = fluency

        # 生成改进建议
        analysis["improvement_suggestions"] = self._generate_suggestions(user_input, expected_patterns)

        return analysis

    def _calculate_accuracy(self, user_input: str, expected_patterns: List[str]) -> float:
        """计算回答准确性"""
        score = 0
        for pattern in expected_patterns:
            if pattern.lower() in user_input.lower():
                score += 1
        return score / len(expected_patterns) if expected_patterns else 0


class EnglishSpeakingCoach(AITrainingCoach):
    def __init__(self):
        super().__init__({})
        self.conversation_scenarios = [
            {
                "situation": "酒店入住",
                "role_play": "你是客人，我是前台",
                "target_vocabulary": ["check in", "reservation", "room key", "amenities"],
                "expected_phrases": ["I have a reservation", "What time is checkout?"]
            },
            {
                "situation": "餐厅点餐",
                "role_play": "你是顾客，我是服务员",
                "target_vocabulary": ["appetizer", "main course", "recommendation", "bill"],
                "expected_phrases": ["What do you recommend?", "Can I have the bill?"]
            }
        ]

    async def practice_dialogue(self, scenario_index: int) -> Dict:
        """对话练习"""
        scenario = self.conversation_scenarios[scenario_index]

        return {
            "scenario": scenario["situation"],
            "role_assignment": scenario["role_play"],
            "target_vocabulary": scenario["target_vocabulary"],
            "ai_opening_line": self._generate_opening_line(scenario),
            "hints": scenario["expected_phrases"]
        }

    async def provide_pronunciation_feedback(self, audio_text: str) -> Dict:
        """发音反馈（需要集成语音识别）"""
        return {
            "pronunciation_score": 85,
            "problematic_sounds": ["th", "r"],
            "practice_words": ["the", "three", "red", "right"],
            "audio_examples": ["..."]  # 发音示范音频
        }


class ProgrammingCoach(AITrainingCoach):
    def __init__(self):
        super().__init__({})
        self.coding_exercises = {
            "beginner": [
                {
                    "title": "两数之和",
                    "description": "编写一个函数，接受两个数字参数并返回它们的和",
                    "test_cases": [
                        {"input": [2, 3], "expected": 5},
                        {"input": [-1, 1], "expected": 0}
                    ],
                    "hints": ["记得处理负数情况", "使用return语句返回结果"]
                }
            ],
            "intermediate": [
                {
                    "title": "斐波那契数列",
                    "description": "实现一个生成斐波那契数列的函数",
                    "test_cases": [
                        {"input": 5, "expected": [0, 1, 1, 2, 3]},
                        {"input": 8, "expected": [0, 1, 1, 2, 3, 5, 8, 13]}
                    ]
                }
            ]
        }

    async def provide_code_review(self, user_code: str, exercise: Dict) -> Dict:
        """代码审查和反馈"""
        review = {
            "correctness": False,
            "efficiency_score": 0,
            "code_quality_issues": [],
            "best_practices_suggestions": [],
            "optimized_solution": ""
        }

        # 运行测试用例
        test_results = await self._run_test_cases(user_code, exercise["test_cases"])
        review["correctness"] = all(test_results)

        # 代码质量分析
        review["code_quality_issues"] = self._analyze_code_quality(user_code)
        review["efficiency_score"] = self._analyze_efficiency(user_code)

        return review


class InterviewCoach(AITrainingCoach):
    def __init__(self):
        super().__init__({})
        self.interview_questions = {
            "tech": [
                "请介绍你最熟悉的项目",
                "如何处理技术上的挑战？",
                "你的职业规划是什么？"
            ],
            "behavioral": [
                "请描述一次你解决冲突的经历",
                "你如何应对工作压力？",
                "你最大的优点和缺点是什么？"
            ]
        }

    async def conduct_mock_interview(self, question_type: str) -> Dict:
        """模拟面试"""
        question = self._select_question(question_type)

        return {
            "question": question,
            "evaluation_criteria": [
                "回答结构是否清晰",
                "是否有具体事例支撑",
                "表达是否自信流畅",
                "是否突出个人优势"
            ],
            "time_limit": 180  # 3分钟思考时间
        }

    async def evaluate_interview_answer(self, user_answer: str, question: str) -> Dict:
        """评估面试回答"""
        evaluation = {
            "content_score": 0,
            "structure_score": 0,
            "delivery_score": 0,
            "star_feedback": "",  # 情境-任务-行动-结果分析
            "improvement_suggestions": [],
            "model_answer": ""
        }

        # 内容相关性分析
        evaluation["content_score"] = self._analyze_content_relevance(user_answer, question)

        # 回答结构分析
        evaluation["structure_score"] = self._analyze_answer_structure(user_answer)

        # 生成STAR模型反馈
        evaluation["star_feedback"] = self._provide_star_feedback(user_answer)

        return evaluation


async def main():
    # 创建英语陪练实例
    english_coach = EnglishSpeakingCoach()

    # 初始化会话
    session_info = await english_coach.initialize_session("user123", TrainingType.LANGUAGE)
    print(f"欢迎消息: {session_info['welcome_message']}")

    # 开始对话练习
    scenario = await english_coach.practice_dialogue(0)
    print(f"场景: {scenario['scenario']}")
    print(f"AI开场: {scenario['ai_opening_line']}")

    # 用户回复
    user_response = "I have a reservation under John Smith"

    # 获取评估反馈
    feedback = await english_coach.evaluate_response(
        user_response,
        scenario["expected_phrases"]
    )

    print(f"准确度评分: {feedback['accuracy_score']}")
    print(f"改进建议: {feedback['improvement_suggestions']}")


# 运行示例
if __name__ == "__main__":
    asyncio.run(main())